The business bestseller lists have showcased several high-profile books featuring case studies of companies enjoying competitive advantage from their cutting-edge use of analytics. Pick one of those case studies, any one you like, and find someone who works at that company – the department and job don’t matter. Use any excuse you like to start a conversation, and give your contact the opportunity to vent about the professional issues of the day. It’s a safe bet that you’ll discover evidence that the company isn’t using analytics consistently and well across the organization.
The business bestseller lists have showcased several high-profile books featuring case studies of companies enjoying competitive advantage from their cutting-edge use of analytics. Pick one of those case studies, any one you like, and find someone who works at that company – the department and job don’t matter. Use any excuse you like to start a conversation, and give your contact the opportunity to vent about the professional issues of the day. It’s a safe bet that you’ll discover evidence that the company isn’t using analytics consistently and well across the organization.
The companies enjoying the greatest successes with analytics are still missing many obvious opportunities to use and benefit from analytics, even though they have hard evidence of the benefits, as well as in-house expertise and other resources. If that’s going on in the cream of the crop, imagine the level of neglect among the vast majority of businesses, the ones who aren’t profiled in bestsellers.
The techniques driving analytics success stories are, for the most part, pretty old. The information is easy to get, and cheap. Every library has it, and there’s a good chance it’s sitting on your bookshelf right now. Pick up any introductory statistics textbook written in the past 50 years and you’ll find all the technical details required for an analytics program that would place most any business way ahead of the pack.
Every executive has easy access to this information, so why is it that most of them don’t use it?
Analytics is hard. Not “end hunger forever” hard or “bring about world peace” hard. Not even “put a man on the moon” hard or “invent an oral contraceptive” hard. But not a piece of cake, either. Analysis requires thoughtful, sustained effort. Serious use of analytics requires at least as much effort as say, good accounting. Many businesses, daresay most of them, put significant effort into good accounting. Do they do this just because it is a good practice? Heck no. There are powers requiring them to do it – governments and investors – and they have means of penalizing businesses who fail to meet these requirements.
It’s not just the analysis itself that is challenging. Analysts need raw material – data. Accurate, clean data is the backbone of a serious analytics program. IT leaders are quick to point out that meeting analysts’ requirements for data and processing is no trivial matter.
Those aren’t the really hard parts, though. Volumes of squeaky clean data and an army of the finest analysts mean nothing unless decision makers are prepared to make decisions based on analytics.
What does it mean to make data-driven decisions?
Say you’re an executive about to invest in a new marketing program. The data-driven way to go about it would be to set measurable goals at the start, then carefully track campaigns and results. Decisions would clearly follow; if sales meet the goal set in advance, declare success and keep on doing more of that program; if sales don’t meet the goal, openly accept that the program didn’t live up to expectations, take your lumps and move on to something else. That’s simple, right? In theory, it is, but in practice, it’s not simple at all.
Executives like to feel important, confident and above all, powerful. (You may remember that statement from my 2010 article, “Talk Analytics with Executives: 4 Things You Must Understand,” which outlines the basics of communicating technical information with decision makers.) Now, how does that play with data-driven decision making? A decision is reduced to a simple, pre-determined rule. Once the rule is established, the decision is out of the executive’s hands – so the executive no longer feels important. If the campaign fails, the failure will be clearly documented, chipping away at the executive’s reputation and sense of confidence. Moving from gut-feel decision making to data-driven making doesn’t play into the average executive’s sense of power.
Knowing that executives resist data-driven decision making, and knowing why, what can you do to nudge them in the right direction?
Start small.
Success with small projects builds confidence step-by that makes the decision-maker feel comfortable using analytics for decision-making. Step-by-step, the size and risk level of associated with analytics projects can be increased in keeping with the comfort level of the decision-maker. Over time, the decision-maker will not only develop greater and greater acceptance of analytics, but may even come to feel uncomfortable making decisions without the support of analytics.
Steer toward low-risk decisions.
Give the decision maker opportunities to become comfortable with analytics by introducing it in connection with low-risk decisions. For example, which results in better conversion – a white mailer envelope or tan? One may perform much better than the other, bringing in greater return on investment. Or perhaps there is no measurable difference in performance – that would also be useful information, and would steer the choice toward the less-costly option, again providing the opportunity for improved returns? Other good starting points might include testing ad copy and images, time of day for releasing email campaigns, or comparing a discount to a bonus gift offer. With decisions like these, the analytics will always offer the decision-maker a way to improve the bottom line without significant risk to reputation or finances.
Speak their language.
Executives are not motivated to learn your lingo, so if you want to influence them, learn theirs! Focus on the issues that affect your decision maker, and lay it down in plain English. Avoid terminology that might be unfamiliar, and if you absolutely must use technical terms, explain those terms and bring them down to earth.
There’s much more on good practices for presentations in these articles:
Talk Analytics with Executives: 4 Things You Must Understand
Defending Your Analytics: Handling Hecklers
Talk Analytics with Executives – Revisited
We’re Not Artists: The Craft of Influencing Decision Makers
It’s true: executives don’t like analytics. But you can guide them to an appreciation of data-driven decision making by introducing the ideas step-by-step and building understanding and comfort along the way. Remember – start small, focus on low-risk decisions at first, and learn to speak their language!
©2011 Meta S. Brown